engineering

Self-tuning

Self-tuning is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Self-tuning rather than just read about it. In short: In control theory a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfilment of an objective function; typically the maximization of efficiency or error minimization. Self-tuning and auto-tuning often refer to the same concept.

Key takeaways

Self-tuning belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Self-tuning to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Self-tuning from memory before moving on to harder problems.

Reference excerpt

In control theory a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfilment of an objective function; typically the maximization of efficiency or error minimization. Self-tuning and auto-tuning often refer to the same concept. Many software research groups consider auto-tuning the proper nomenclature. Self-tuning systems typically exhibit non-linear adaptive control. Self-tuning systems have been a hallmark of the aerospace industry for decades, as this sort of feedback is necessary to generate optimal multi-variable control for non-linear processes. In the telecommunications industry, adaptive communications are often used to dynamically modify operational system parameters to maximize efficiency and robustness.

Examples Examples of self-tuning systems in computing include:

TCP (Transmission Control Protocol) Microsoft SQL Server (Newer implementations only) FFTW (Fastest Fourier Transform in the West) ATLAS (Automatically Tuned Linear Algebra Software) libtune (Tunables library for Linux) PhiPAC (Self Tuning Linear Algebra Software for RISC) MILEPOST GCC (Machine learning based self-tuning compiler) Performance benefits can be substantial. Professor Jack Dongarra, an American computer scientist, claims self-tuning boosts performance, often on the order of 300%. Digital self-tuning controllers are an example of self-tuning systems at the hardware level.

Architecture Self-tuning systems are typically composed of four components: expectations, measurement, analysis, and actions. The expectations describe how the system should behave given exogenous conditions. Measurements gather data about the conditions and behaviour. Analysis helps determine whether the expectations are being met- and which subsequent actions should be performed. Common actions are gathering more data and performing dynamic reconfiguration of the system. Self-tuning (self-adapting) systems of automatic control are systems whereby adaptation to randomly changing conditions is performed by means of automatically changing parameters or via automatically determining their optimum configuration. In any non-self-tuning automatic control system there are parameters which have an influence on system stability and control quality and which can be tuned. If these parameters remain constant whilst operating conditions (such as input signals or different characteristics of controlled objects) are substantially varying, control can degrade or even become unstable. Manual tuning is often cumbersome and sometimes impossible. In such cases, not only is using self-tuning systems technically and economically worthwhile, but it could be the only means of robust control. Self-tuning systems can be with or without parameter determination. In systems with parameter determination the required level of control quality is achieved by automatically searching for an optimum (in some sense) set of parameter values. Control quality is described by a generalised characteristic which is usually a complex and not completely known or stable function of the primary parameters. This characteristic is either measured directly or computed based on the primary parameter values. The parameters are then tentatively varied. An analysis of the control quality characteristic oscillations caused by the varying of the parameters makes it possible to figure out if the parameters have optimum values, i.e.. if those values deliver extreme (minimum or maximum) values of the control quality characteristic. If the characteristic values deviate from an extremum, the parameters need to be varied until optimum values are found. Self-tuning systems with parameter determination can reliably operate in environments characterised by wide variations of exogenous conditions. In practice systems with parameter determination require considerable time to find an optimum tuning, i.e. time necessary for self-tuning in such systems is bounded from below. Self-tuning systems without parameter determination do not have this disadvantage. In such systems, some characteristic of control quality is used (e.g., the first time derivative of a controlled parameter). Automatic tuning makes sure that this characteristic is kept within given bounds. Different self-tuning systems without parameter determination exist that are based on controlling transitional processes, frequency characteristics, etc. All of those are examples of closed-circuit self-tuning systems, whereby parameters are automatically corrected every time the quality characteristic value falls outside the allowable bounds. In contrast, open-circuit self-tuning systems are systems with para-metrical compensation, whereby input signal itself is controlled and system parameters are changed according to a specified procedure. This type of self-tuning can be close to instantaneous. However, in order to realise such self-tuning one needs to control the environment in which the system operates and a good enough understanding of how the environment influences the controlled system is required. In practice self-tuning is done through the use of specialised hardware or adaptive software algorithms. Giving software the ability to self-tune (adapt):

Facilitates controlling critical processes of systems; Approaches optimum operation regimes; Facilitates design unification of control systems; Shortens the lead times of system testing and tuning; Lowers the criticality of technological requirements on control systems by making the systems more robust; Saves personnel time for system tuning.

Metaheuristics

Literature

External links Using Probabilistic Reasoning to Automate Software Tuning Frigo, M. and Johnson, S. G., "The design and implementation of FFTW3", Proceedings of the IEEE, 93(2), February 2005, 216 - 231. doi:10.1109/JPROC.2004.840301. Optimizing Matrix Multiply using PHiPAC: a Portable, High-Performance, ANSI C Coding Methodology Faster than a Speeding Algorithm Archived 2018-07-03 at the Wayback Machine Rethinking Database System Architecture: Towards a Self-tuning RISC-style Database System Self-Tuning Systems Software Archived 2016-10-04 at the Wayback Machine Microsoft Research Adds Data Mining and Self-tuning Technology to SQL Server 2000 A Comparison of TCP Automatic Tuning Techniques for Distributed Computing Tunables library for Linux A Review of Relay Auto-tuning Methods for the Tuning of PID-type Controllers

Worked examples

Example 1 — a first encounter with Self-tuning

Start with the simplest possible case. Write down what Self-tuning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Self-tuning before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Self-tuning ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Self-tuning

In research: Self-tuning appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Self-tuning in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Self-tuning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Control engineering, Control theory, Electronic feedback, so understanding it makes those chapters shorter.
In everyday life: Look for Self-tuning outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Self-tuning in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Self-tuning means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Self-tuning out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Self-tuning in simple terms?

Why does Self-tuning matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Self-tuning?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Self-tuning.